We built our density map estimation network based on the Congested Scene Recognition Network (CSRNet) [1]}. Here we briefly review some of the features characterizing this algorithm. For a more detailed description see also sec:back:visual-counting:cnn-based. CSRNet provides a cnn-based method that can understand highly congested scenes and perform accurate density estimation and counting. It is composed of two major components. The authors use the well-known VGG-16 network [2]} as the front-end for 2D feature extraction because of its strong transfer learning ability.
On the other hand, the back-end consists of dilated kernels. The basic concept of dilated convolutions is to deliver larger reception fields replacing the pooling operations. It is worth noting that the max pool operation is responsible for losing quality in the density generation procedure. Since the output size from VGG is reduced by a factor of 8 of the original input size, we up-sampled the final output to compare it with the ground truth density map.
